LDA-guided search engine for the nonsubjective analysis of infrared microscopic maps

Download
  1. Get@NRC: LDA-guided search engine for the nonsubjective analysis of infrared microscopic maps (Opens in a new window)
DOIResolve DOI: http://doi.org/10.1366/0003702991945920
AuthorSearch for: ; Search for: ; Search for: ; Search for: ; Search for:
TypeArticle
Journal titleApplied Spectroscopy
ISSN0003-7028
1943-3530
Volume53
Issue11
Pages13231330; # of pages: 8
SubjectInfrared microscopy; Linear discriminant analysis; Analysis of tissue; Nonsubjective analysis of spectroscopic maps
AbstractAcquisition of large data sets from human tissues by infrared (IR) microscopy is now routine. However, processing such large data sets, which may contain more than 10 000 spectra, provides an enormous challenge. Overcoming this challenge and developing nonsubjective methods for the analysis of IR microscopic results remain the major hurdle to developing clinically useful applications. A three-step pattern recognition strategy based upon linear discriminant analysis has been developed for use as a search engine for tissue characterization. The three-step strategy includes a genetic algorithm-guided data reduction step, a classification step based upon linear discriminant analysis, and a final step in which the discriminant coefficients are converted into a visually appealing, nonsubjective representation of the distribution of each class throughout the tissue section. The application of this search engine in the characterization of tumor-bearing skin is demonstrated.
Publication date
PublisherSociety for Applied Spectroscopy
AffiliationNational Research Council Canada; NRC Institute for Biodiagnostics
Peer reviewedYes
NRC number947
NPARC number9742188
Export citationExport as RIS
Report a correctionReport a correction
Record identifier9b724570-940b-4b53-b3f8-2ee46307624c
Record created2009-07-17
Record modified2016-10-11
Bookmark and share
  • Share this page with Facebook (Opens in a new window)
  • Share this page with Twitter (Opens in a new window)
  • Share this page with Google+ (Opens in a new window)
  • Share this page with Delicious (Opens in a new window)